CrossValidationReport.metrics.brier_score#

CrossValidationReport.metrics.brier_score(*, data_source='test', aggregate=None)[source]#

Compute the Brier score.

Parameters:
data_source{“test”, “train”}, default=”test”

The data source to use.

  • “test” : use the test set provided when creating the report.

  • “train” : use the train set provided when creating the report.

aggregate{“mean”, “std”} or list of such str, default=None

Function to aggregate the scores across the cross-validation splits.

Returns:
pd.DataFrame

The Brier score.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from skore import CrossValidationReport
>>> X, y = load_breast_cancer(return_X_y=True)
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = CrossValidationReport(classifier, X=X, y=y, cv_splitter=2)
>>> report.metrics.brier_score()
                LogisticRegression
                        Split #0  Split #1
Metric
Brier score             0.04...   0.04...